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Found 9 papers, 3 papers with code
Ultra Low Complexity Deep Learning Based Noise Suppression
This paper introduces an innovative method for reducing the computational complexity of deep neural networks in real-time speech enhancement on resource-constrained devices.
New Insights on Target Speaker Extraction
However, such studies have been conducted on a few datasets and have not considered recent deep neural network architectures for SS that have shown impressive separation performance.
An Empirical Study of Visual Features for DNN based Audio-Visual Speech Enhancement in Multi-talker Environments
Audio-visual speech enhancement (AVSE) methods use both audio and visual features for the task of speech enhancement and the use of visual features has been shown to be particularly effective in multi-speaker scenarios.
Multi-scale aggregation of phase information for reducing computational cost of CNN based DOA estimation
In this work, we propose to use systematic dilations of the convolution filters in each of the convolution layers of the previously proposed CNN for expansion of the receptive field of the filters to reduce the computational cost of the method.
CountNet: Estimating the Number of Concurrent Speakers Using Supervised Learning Speaker Count Estimation
Estimating the maximum number of concurrent speakers from single-channel mixtures is a challenging problem and an essential first step to address various audio-based tasks such as blind source separation, speaker diarization, and audio surveillance.
Multi-Speaker DOA Estimation Using Deep Convolutional Networks Trained with Noise Signals
Supervised learning based methods for source localization, being data driven, can be adapted to different acoustic conditions via training and have been shown to be robust to adverse acoustic environments.
Multi-Speaker Localization Using Convolutional Neural Network Trained with Noise
The problem of multi-speaker localization is formulated as a multi-class multi-label classification problem, which is solved using a convolutional neural network (CNN) based source localization method.
Classification vs. Regression in Supervised Learning for Single Channel Speaker Count Estimation
The task of estimating the maximum number of concurrent speakers from single channel mixtures is important for various audio-based applications, such as blind source separation, speaker diarisation, audio surveillance or auditory scene classification.
Audio and Speech Processing Sound
Broadband DOA estimation using Convolutional neural networks trained with noise signals
Since only the phase component of the input is used, the CNN can be trained with synthesized noise signals, thereby making the preparation of the training data set easier compared to using speech signals.
